# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Initializer for cell parameters."""
import numbers
import math
from functools import reduce
import numpy as np
from scipy.stats import truncnorm
from .seed import get_seed, _get_graph_seed
from . import dtype as mstype
from .tensor import Tensor
from .._c_expression import random_normal
_INITIALIZER_ALIAS = dict()
[docs]class Initializer:
"""
The base class of the initializer.
Initialization of tensor basic attributes and model weight values.
Args:
kwargs (dict): Keyword arguments for Initializer.
Returns:
Array, an array after being initialized.
"""
def __init__(self, **kwargs):
self._kwargs = kwargs
self._seed = None
@property
def seed(self):
if self._seed is None:
seed, seed2 = _get_graph_seed(get_seed(), "init")
else:
seed, seed2 = self._seed + 1, 0
return seed, seed2
@seed.setter
def seed(self, value):
self._seed = value
def _initialize(self, *kwargs):
raise NotImplementedError('Must be overridden!')
def __call__(self, arr):
return self._initialize(arr)
def _register(*aliases):
"""Return the alias register."""
def alias_reg(cls):
name = cls.__name__
name = name.lower()
if name not in _INITIALIZER_ALIAS:
_INITIALIZER_ALIAS[name] = cls
for alias in aliases:
if alias not in _INITIALIZER_ALIAS:
_INITIALIZER_ALIAS[alias] = cls
return cls
return alias_reg
def _assignment(arr, num):
"""Assign the value of `num` to `arr`."""
if arr.shape == ():
arr = arr.reshape((1))
arr[:] = num
arr = arr.reshape(())
else:
if isinstance(num, np.ndarray):
arr[:] = num[:]
else:
arr[:] = num
return arr
[docs]@_register('zeros')
class Zero(Initializer):
"""
Initialize the array to zero.
Args:
arr (Array): The array to be assigned.
Returns:
Array, an array after being assigned.
"""
def _initialize(self, arr):
_assignment(arr, 0)
[docs]@_register('ones')
class One(Initializer):
"""
Initialize the array to one.
Args:
arr (Array): The array to be assigned.
Returns:
Array, assigned array.
"""
def _initialize(self, arr):
_assignment(arr, 1)
def _calculate_fan_in_and_fan_out(shape):
"""
calculate fan_in and fan_out
Args:
shape (tuple): input shape.
Returns:
Tuple, a tuple with two elements, the first element is `n_in` and the second element is `n_out`.
"""
dimensions = len(shape)
if dimensions < 2:
raise ValueError("Fan in and fan out can not be computed for tensor with fewer than 2 dimensions")
if dimensions == 2: # Linear
fan_in = shape[1]
fan_out = shape[0]
else:
num_input_fmaps = shape[1]
num_output_fmaps = shape[0]
receptive_field_size = 1
if dimensions > 2:
receptive_field_size = shape[2] * shape[3]
fan_in = num_input_fmaps * receptive_field_size
fan_out = num_output_fmaps * receptive_field_size
return fan_in, fan_out
def _calculate_correct_fan(shape, mode):
"""
Calculate fan.
Args:
shape (tuple): input shape.
mode (str): only support fan_in and fan_out.
Returns:
fan_in or fan_out.
"""
mode = mode.lower()
valid_modes = ['fan_in', 'fan_out']
if mode not in valid_modes:
raise ValueError("Mode {} not supported, please use one of {}".format(mode, valid_modes))
fan_in, fan_out = _calculate_fan_in_and_fan_out(shape)
return fan_in if mode == 'fan_in' else fan_out
def _calculate_gain(nonlinearity, param=None):
"""
Calculate gain.
Args:
nonlinearity (str): nonlinearity function.
param (str): used to calculate negative_slope.
Returns:
number.
"""
linear_fns = ['linear', 'conv1d', 'conv2d', 'conv3d', 'conv_transpose1d', 'conv_transpose2d', 'conv_transpose3d']
if nonlinearity in linear_fns or nonlinearity == 'sigmoid':
res = 1
elif nonlinearity == 'tanh':
res = 5.0 / 3
elif nonlinearity == 'relu':
res = math.sqrt(2.0)
elif nonlinearity == 'leaky_relu':
if param is None:
negative_slope = 0.01
elif not isinstance(param, bool) and isinstance(param, int) or isinstance(param, float):
# True/False are instances of int, hence check above
negative_slope = param
else:
raise ValueError("negative_slope {} not a valid number".format(param))
res = math.sqrt(2.0 / (1 + negative_slope ** 2))
else:
raise ValueError("Unsupported nonlinearity {}".format(nonlinearity))
return res
def _calculate_in_and_out(arr):
"""
Calculate n_in and n_out.
Args:
arr (Array): Input array.
Returns:
Tuple, a tuple with two elements, the first element is `n_in` and the second element is `n_out`.
"""
dim = len(arr.shape)
if dim < 2:
raise ValueError("If initialize data with xavier uniform, the dimension of data must be greater than 1.")
n_in = arr.shape[1]
n_out = arr.shape[0]
if dim > 2:
counter = reduce(lambda x, y: x * y, arr.shape[2:])
n_in *= counter
n_out *= counter
return n_in, n_out
[docs]@_register('he_normal')
class HeNormal(Initializer):
r"""
Initialize the array with He kaiming Normal algorithm, and from a normal distribution collect samples within
N(0, sigma).
Args:
negative_slope (int, float, bool): The negativa slope of the rectifier used after this layer
(only used when `nonlinearity` is 'leaky_relu'). Default: 0.
mode (str): Either 'fan_in' or 'fan_out'. Choosing 'fan_in' preserves the magnitude of the
variance of the weights in the forward pass. Choosing 'fan_out' preserves the magnitudes
in the backwards pass. Default: fan_in.
nonlinearity (str): The non-linear function, recommended to use only with 'relu' or 'leaky_relu'.
Default: leaky_relu.
Returns:
Array, assigned array.
"""
def __init__(self, negative_slope=0, mode='fan_in', nonlinearity='leaky_relu'):
super(HeNormal, self).__init__(negative_slope=negative_slope, mode=mode, nonlinearity=nonlinearity)
self.negative_slope = negative_slope
self.mode = mode
self.nonlinearity = nonlinearity
def _initialize(self, arr):
fan = _calculate_correct_fan(arr.shape, self.mode)
gain = _calculate_gain(self.nonlinearity, self.negative_slope)
std = gain / math.sqrt(fan)
data = np.random.normal(0, std, arr.shape)
_assignment(arr, data)
[docs]class Constant(Initializer):
"""
Initialize a constant.
Args:
value (Union[int, numpy.ndarray]): The value to initialize.
Returns:
Array, an array after being assigned.
"""
def __init__(self, value):
super(Constant, self).__init__(value=value)
self.value = value
def _initialize(self, arr):
_assignment(arr, self.value)
[docs]@_register()
class Normal(Initializer):
"""
Initialize a normal array, and obtain values N(0, sigma) from the uniform distribution
to fill the input tensor.
Args:
sigma (float): The sigma of the array. Default: 0.01.
Returns:
Array, normal array.
"""
def __init__(self, sigma=0.01):
super(Normal, self).__init__(sigma=sigma)
self.sigma = sigma
def _initialize(self, arr):
seed, seed2 = self.seed
output_tensor = Tensor(np.zeros(arr.shape, dtype=np.float32))
random_normal(arr.shape, seed, seed2, output_tensor)
output_data = output_tensor.asnumpy()
output_data *= self.sigma
_assignment(arr, output_data)
[docs]@_register()
class TruncatedNormal(Initializer):
"""
Initialize a truncated normal distribution which is a bounded normal distribution within N(low, high).
Args:
sigma (float): The sigma of the array. Default: 0.01.
Returns:
Array, truncated normal array.
"""
def __init__(self, sigma=0.01):
super(TruncatedNormal, self).__init__(sigma=sigma)
self.sigma = sigma
def _initialize(self, arr):
tmp = truncnorm.rvs(-2, 2, loc=0, scale=self.sigma, size=arr.shape, random_state=None)
_assignment(arr, tmp)
[docs]def initializer(init, shape=None, dtype=mstype.float32):
"""
Create and initialize a tensor.
Args:
init (Union[Tensor, str, Initializer, numbers.Number]): Initialize value.
- `str`: The `init` should be the alias of the class inheriting from `Initializer` and the corresponding
class will be called.
- `Initializer`: The `init` should be the class inheriting from `Initializer` to initialize tensor.
- `numbers.Number`: The `Constant` will be called to initialize tensor.
shape (Union[tuple, list, int]): A list of integers, a tuple of integers or an integer as the shape of
output. Default: None.
dtype (:class:`mindspore.dtype`): The type of data in initialized tensor. Default: mindspore.float32.
Returns:
Union[Tensor], return is Tensor object.
Examples:
>>> import mindspore
>>> from mindspore.common.initializer import initializer, One
>>> tensor = initializer('ones', [1, 2, 3], mindspore.float32)
>>> tensor = initializer(One(), [1, 2, 3], mindspore.float32)
>>> tensor = initializer(0, [1, 2, 3], mindspore.float32)
"""
if not isinstance(init, (Tensor, numbers.Number, str, Initializer)):
raise TypeError("Unsupported init type '{}'.".format(type(init)))
if isinstance(init, Tensor):
init_shape = init.shape
shape = shape if isinstance(shape, (tuple, list)) else [shape]
if shape is not None and init_shape != tuple(shape):
raise ValueError("The shape of init should be same as variable shape, but got the shape of init {} and "
"the variable shape {}.".format(list(init.shape), shape))
return init
if isinstance(shape, list):
shape = tuple(shape)
elif isinstance(shape, numbers.Number):
shape = (shape,)
for value in shape if shape is not None else ():
if not isinstance(value, int) or value <= 0:
raise ValueError(f"shape is invalid, shape value must be positive integer, shape:{shape}")
if isinstance(init, str):
init = _INITIALIZER_ALIAS[init.lower()]()
if init is None:
raise ValueError("The class corresponding to '{}' was not found.".format(init))
elif isinstance(init, numbers.Number):
init = Constant(init)
shape = shape if shape is not None else init.shape
init_obj = Tensor(dtype=dtype, shape=shape, init=init)
return init_obj
__all__ = [
'Initializer',
'initializer',
'TruncatedNormal',
'Normal',
'Uniform',
'HeUniform',
'HeNormal',
'XavierUniform',
'One',
'Zero',
'Constant']